TW-SIR: time-window based SIR for COVID-19 forecasts

Abstract Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations b...

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Autores principales:	Zhifang Liao, Peng Lan, Zhining Liao, Yan Zhang, Shengzong Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f0dce2406c424478ae7f89545f5b4922
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spelling	oai:doaj.org-article:f0dce2406c424478ae7f89545f5b49222021-12-02T13:46:38ZTW-SIR: time-window based SIR for COVID-19 forecasts10.1038/s41598-020-80007-82045-2322https://doaj.org/article/f0dce2406c424478ae7f89545f5b49222020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80007-8https://doaj.org/toc/2045-2322Abstract Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries–––China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.Zhifang LiaoPeng LanZhining LiaoYan ZhangShengzong LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-15 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhifang Liao Peng Lan Zhining Liao Yan Zhang Shengzong Liu TW-SIR: time-window based SIR for COVID-19 forecasts
description	Abstract Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries–––China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.
format	article
author	Zhifang Liao Peng Lan Zhining Liao Yan Zhang Shengzong Liu
author_facet	Zhifang Liao Peng Lan Zhining Liao Yan Zhang Shengzong Liu
author_sort	Zhifang Liao
title	TW-SIR: time-window based SIR for COVID-19 forecasts
title_short	TW-SIR: time-window based SIR for COVID-19 forecasts
title_full	TW-SIR: time-window based SIR for COVID-19 forecasts
title_fullStr	TW-SIR: time-window based SIR for COVID-19 forecasts
title_full_unstemmed	TW-SIR: time-window based SIR for COVID-19 forecasts
title_sort	tw-sir: time-window based sir for covid-19 forecasts
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/f0dce2406c424478ae7f89545f5b4922
work_keys_str_mv	AT zhifangliao twsirtimewindowbasedsirforcovid19forecasts AT penglan twsirtimewindowbasedsirforcovid19forecasts AT zhiningliao twsirtimewindowbasedsirforcovid19forecasts AT yanzhang twsirtimewindowbasedsirforcovid19forecasts AT shengzongliu twsirtimewindowbasedsirforcovid19forecasts
_version_	1718392533011136512

TW-SIR: time-window based SIR for COVID-19 forecasts

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